基于支持向量机的多类分类研究

现今流行的分类方法的重要基础是传统的统计学，前提是要有足够的样本，当样本数目有限时容易出现过学习的问题，导致分类效果不理想。引入支持向量机方法，它基于统计学习理论，采用了结构风险最小化原则代替经验风险最小化原则，较好的解决了小样本学习的问题；又由于采用了核函数思想，把非线性空间的问题转换到线性空间，降低了算法的复杂度。对其相关内容包括优化算法及多类分类问题的解决进行了研究，最后用一个实例说明了该方法的可行性和有效性。

Research on multi-classification based on support vector machine

Most of the existing methods are based on traditional statistics,which provides that conclusion only for the situation where sample size is tending to infinity.So they may not work well in practical case with limi-ted samples and easily lead to the problem of overfilling.This paper introduced the support vector machine(SVM) based on the theory of traditional statistics.This method can solve small-sample learning problems better by using experiential risk minimization(ERM) in place of structural risk minimization(SRM).Moreover,this theory can change the problem in non-linearity space to that in the linearity space in order to reduce the algorithm complexity by using the kernel function idea.It studies some relational contents including the optimization algorithm and the solution to multi-classification.Finally,through an example,it shows that the pro-posed method is effective and feasible.